欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
轴承复合故障类型多样,且部分故障的特征频率相近噪声污染严重.采用经验模态分解(EMD)的方法,在强噪声背景下会引起相近频率故障成分的无法识别,同时也难以提取微弱的故障信号.由此,提出一种基于能量聚集性的轴承复合故障诊断方法.首先借助离散余弦变换(DCT)的频域能量聚集性和奇异值分解(SVD)的时域能量聚集性,对轴承复合故障信号进行预处理,实现降噪并分离频率相近的微弱故障信号.然后对分离出来的不同故障信号进行经验模态分解,去除伪分量,对剩余的本征模态函数进行频谱分析.最后,根据本征模态函数的频谱诊断故障.仿真信号和实测轴承故障诊断信号分析表明,与直接使用EMD进行轴承复合故障诊断相比,该方法能够在强背景噪声下准确分离频率相近的微弱故障分量,改善复合故障诊断的准确性.
英文摘要：
Combined bearing fault types are various, which cause serious noise pollution. Since the characteristic frequencies of several fault signals are close, these fault signals can not be recognized effectively in strong noise background if the empirical mode decomposition (EMD) method is used only. Meanwhile the weak fault can hardly be extracted. In this paper, a bearing combined fault diagnosis scheme based on energy aggregation was proposed. Firstly, in virtue of the energy aggregation in frequency domain of discrete cosine transform (DCT) and the energy aggregation in time domain of singular value decomposition (SVD), the combined fault signals were preprocessed to separate the weak fault signals whose frequencies are close. Then, the separated signals were analyzed by EMD, the false intrinsic mode functions (IMFs) found by the energy analysis were deleted and the frequency spectra of the ture IMFs were analyzed. Finally, the combined faults were diagnosed by IMFs spectra. Results of the simulated signal and the actual bearing fault signal analyses show that compared with EMD, this method can resolute adjacent frequency fault signals and distill weak signals in strong noise background, and detect the combined faults precisely.
同期刊论文项目
同项目期刊论文
Engine fault diagnosis based on a morphological neural network using a morphological filter as a pre
Classification of time-frequency representations using improved morphological pattern spectrum for e
Research on Oil Atomic Spectrometric Data Semi-Supervised Fuzzy C-Means Clustering Based on Parzen W
期刊信息
位置：